


"""simple docstring"""



import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler







@dataclass
class    __a						( __snake_case							):
				lowerCamelCase     :       int			       =0
				lowerCamelCase     :       bool			       =False
				lowerCamelCase     :       float			       =3.0







class    __a						( unittest.TestCase							):







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					self.assertDictEqual(MockClass().to_kwargs()      ,	{}   )
					self.assertDictEqual(MockClass(a=2   ).to_kwargs()      ,	{'''a''': 2}   )
					self.assertDictEqual(MockClass(a=2      ,	b=UpperCAmelCase   ).to_kwargs()      ,	{'''a''': 2, '''b''': True}   )
					self.assertDictEqual(MockClass(a=2      ,	c=2.2_5   ).to_kwargs()      ,	{'''a''': 2, '''c''': 2.2_5}   )







				@require_cuda
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			GradScalerKwargs(init_scale=1024      ,	growth_factor=2   )
					AcceleratorState._reset_state()
					lowerCAmelCase_      							=			Accelerator(mixed_precision='''fp16'''      ,	kwargs_handlers=[scaler_handler]   )
					print(accelerator.use_fpaa   )
					lowerCAmelCase_      							=			accelerator.scaler

					# Check the kwargs have been applied
					self.assertEqual(scaler._init_scale      ,	1_0_2_4.0   )
					self.assertEqual(scaler._growth_factor      ,	2.0   )

					# Check the other values are at the default
					self.assertEqual(scaler._backoff_factor      ,	0.5   )
					self.assertEqual(scaler._growth_interval      ,	2000   )
					self.assertEqual(scaler._enabled      ,	UpperCAmelCase   )







				@require_multi_gpu
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			['''torchrun''', F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__   )]
					execute_subprocess_async(UpperCAmelCase      ,	env=os.environ.copy()   )


if __name__ == "__main__":
		lowercase_							=							DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
		lowercase_							=							Accelerator(kwargs_handlers=[ddp_scaler])
		lowercase_							=							torch.nn.Linear(1_00, 2_00)
		lowercase_							=							accelerator.prepare(model)

		# Check the values changed in kwargs
		lowercase_							=							''
		lowercase_							=							model.bucket_bytes_cap // (10_24 * 10_24)
		if observed_bucket_cap_map != 15:
				error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
		if model.find_unused_parameters is not True:
				error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

		# Check the values of the defaults
		if model.dim != 0:
				error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
		if model.broadcast_buffers is not True:
				error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
		if model.gradient_as_bucket_view is not False:
				error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

		# Raise error at the end to make sure we don't stop at the first failure.
		if len(error_msg) > 0:
				raise ValueError(error_msg)



"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_							=							logging.get_logger(__name__)

lowercase_							=							{
    's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json',
}







class    __a						( __snake_case							):
				lowerCamelCase     :       Union[str, Any]			       ='open-llama'







				def __init__( self      ,	UpperCAmelCase=10_0000      ,	UpperCAmelCase=4096      ,	UpperCAmelCase=1_1008      ,	UpperCAmelCase=32      ,	UpperCAmelCase=32      ,	UpperCAmelCase="silu"      ,	UpperCAmelCase=2048      ,	UpperCAmelCase=0.0_2      ,	UpperCAmelCase=1E-6      ,	UpperCAmelCase=True      ,	UpperCAmelCase=0      ,	UpperCAmelCase=1      ,	UpperCAmelCase=2      ,	UpperCAmelCase=False      ,	UpperCAmelCase=True      ,	UpperCAmelCase=0.1      ,	UpperCAmelCase=0.1      ,	UpperCAmelCase=True      ,	UpperCAmelCase=True      ,	UpperCAmelCase=None      ,	**UpperCAmelCase      ,	):

					'''simple docstring'''






					lowerCAmelCase_      							=			vocab_size
					lowerCAmelCase_      							=			max_position_embeddings
					lowerCAmelCase_      							=			hidden_size
					lowerCAmelCase_      							=			intermediate_size
					lowerCAmelCase_      							=			num_hidden_layers
					lowerCAmelCase_      							=			num_attention_heads
					lowerCAmelCase_      							=			hidden_act
					lowerCAmelCase_      							=			initializer_range
					lowerCAmelCase_      							=			rms_norm_eps
					lowerCAmelCase_      							=			use_cache
					lowerCAmelCase_      							=			kwargs.pop(
					    '''use_memorry_efficient_attention'''      ,	UpperCAmelCase   )
					lowerCAmelCase_      							=			hidden_dropout_prob
					lowerCAmelCase_      							=			attention_dropout_prob
					lowerCAmelCase_      							=			use_stable_embedding
					lowerCAmelCase_      							=			shared_input_output_embedding
					lowerCAmelCase_      							=			rope_scaling
					self._rope_scaling_validation()

					super().__init__(
					    pad_token_id=UpperCAmelCase      ,	bos_token_id=UpperCAmelCase      ,	eos_token_id=UpperCAmelCase      ,	tie_word_embeddings=UpperCAmelCase      ,	**UpperCAmelCase      ,	)







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					if self.rope_scaling is None:
						return

					if not isinstance(self.rope_scaling      ,	UpperCAmelCase   ) or len(self.rope_scaling   ) != 2:
						raise ValueError(
						    '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
						    F"""got {self.rope_scaling}"""   )
					lowerCAmelCase_      							=			self.rope_scaling.get('''type'''      ,	UpperCAmelCase   )
					lowerCAmelCase_      							=			self.rope_scaling.get('''factor'''      ,	UpperCAmelCase   )
					if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
						raise ValueError(
						    F"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""   )
					if rope_scaling_factor is None or not isinstance(UpperCAmelCase      ,	UpperCAmelCase   ) or rope_scaling_factor <= 1.0:
						raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""   )